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Passive Sensing in the Prediction of Suicidal Thoughts and Behaviors: Protocol for a Systematic Review
Tanita Winkler1, Rebekka Büscher2, Mark Erik Larsen3
1Institute of Psychology, University of Freiburg, Freiburg, Germany.
Background:
Suicide is a severe public health problem, resulting in a high number of attempts and deaths each year. Early detection of suicidal thoughts and behaviors (STBs) is key to preventing attempts. We discuss passive sensing of digital and behavioral markers to enhance the detection and prediction of STBs.
Objective:
The paper presents the protocol for a systematic review that aims to summarize existing research on passive sensing of STBs and evaluate whether the STB prediction can be improved using passive sensing compared to prior prediction models.
Methods:
A systematic search will be conducted in the scientific databases MEDLINE, PubMed, Embase, PsycINFO, and Web of Science. Eligible studies need to investigate any passive sensor data from smartphones or wearables to predict STBs. The predictive value of passive sensing will be the primary outcome. The practical implications and feasibility of the studies will be considered as secondary outcomes. Study quality will be assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). If studies are sufficiently homogenous, we will conduct a meta-analysis of the predictive value of passive sensing on STBs.
Results:
The review process started in July 2022 with data extraction in September 2022. Results are expected in December 2022.
Conclusions:
Despite intensive research efforts, the ability to predict STBs is little better than chance. This systematic review will contribute to our understanding of the potential of passive sensing to improve STB prediction. Future research will be stimulated since gaps in the current literature will be identified and promising next steps toward clinical implementation will be outlined.
Trial Registration:
OSF Registries osf-registrations-hzxua-v1; https://osf.io/hzxua.
International Registered Report Identifier (Irrid):
DERR1-10.2196/42146.
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